Running a FastAPI application with an ASGI server like Uvicorn initiates a process that listens for incoming HTTP requests. What happens when a request arrives and a response is sent back? Understanding this request/response cycle provides the foundation for building effective web APIs.
Let's break down the typical process of a request within a FastAPI application:
Request Arrival: A client (like a web browser, a mobile app, or another service using curl or Python's requests library) sends an HTTP request to the server's address and port where your FastAPI application is running (e.g., http://127.0.0.1:8000/items/5?query_param=abc). This request includes:
GET, POST, PUT, DELETE)./items/5).Content-Type, Authorization, etc.).?query_param=abc).POST, PUT, containing data, often in JSON format).ASGI Server Handling: The ASGI server (like Uvicorn) receives the raw HTTP request. It parses the request and translates it into a standardized format known as the ASGI scope, which is essentially a Python dictionary containing all the request details.
FastAPI Takes Over: The ASGI server passes the ASGI scope (and related event messages for receiving the body) to the FastAPI application instance.
Routing: FastAPI examines the request's path (/items/5) and HTTP method (GET). It compares this against the path operations you defined in your application using decorators like @app.get("/items/{item_id}"). It finds the first matching path operation function.
Parameter Parsing and Validation: FastAPI automatically parses parameters from the request based on your function's signature:
{item_id} in /items/{item_id}), FastAPI extracts the corresponding value from the path (5 in our example) and converts it to the type declared in your function signature (e.g., item_id: int).query_param=abc) and matches them to function arguments that are not part of the path. Type hints are used for conversion and basic validation.POST or PUT, if your function expects a body (typically defined using a Pydantic model), FastAPI reads the request body, parses it (usually assuming JSON), and validates it against the Pydantic model. We will cover Pydantic in detail in the next chapter.Dependency Injection: FastAPI's dependency injection system resolves any dependencies declared for the path operation function (more on this in later chapters).
Path Operation Function Execution: FastAPI calls your path operation function (the Python function decorated with @app.get, @app.post, etc.), passing the extracted and validated parameters as arguments. If the function is defined with async def, FastAPI awaits its execution, allowing other requests to be handled concurrently if the function performs I/O operations. If it's a regular def function, FastAPI runs it in an external threadpool to avoid blocking the main event loop (important for CPU-bound tasks like ML inference).
Processing Logic: Your function code executes. This is where you implement the specific logic for the endpoint, such as retrieving data from a database, processing input, or, in the context of this course, loading an ML model and performing inference.
Response Generation: Your function returns a result (e.g., a dictionary, a list, a Pydantic model instance, a string, or a Response object).
Data Conversion: FastAPI takes the return value from your function and converts it into an appropriate HTTP response.
Content-Type header to application/json.fastapi.responses.Response (or subclasses like JSONResponse, HTMLResponse) for finer control over the status code, headers, and body.Response Model Validation (Optional): If you declared a response_model in your path operation decorator, FastAPI validates the outgoing data against this model and filters it, ensuring the response structure matches the defined schema.
Response Transmission: FastAPI sends the finalized HTTP response (status code, headers, body) back to the ASGI server.
Final Delivery: The ASGI server transmits the HTTP response back over the network to the original client.
This cycle repeats for every incoming request. FastAPI's use of type hints, Pydantic, and asynchronous capabilities makes steps like parameter parsing, data validation, and concurrent handling efficient and developer-friendly.
Diagram illustrating the flow of a request from the client through the ASGI server and FastAPI to your application code, and the response back to the client.
Understanding this flow helps in debugging issues, structuring your application logic effectively, and appreciating how FastAPI components work together to serve your APIs, including those designed for machine learning predictions. As we proceed, we'll explore each stage, particularly data validation with Pydantic and the execution of ML models within path operation functions, in greater detail.
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